"""simple docstring"""



from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


SCREAMING_SNAKE_CASE__:str									=			logging.get_logger(__name__)






class  snake_case__      (       snake_case_       ):
 _snake_case       :					List[Any]							  =						["""pixel_values"""]


 def __init__( self    ,		lowerCamelCase = True    ,		lowerCamelCase = 1 / 255    ,		lowerCamelCase = True    ,		lowerCamelCase = 8    ,		**lowerCamelCase    ,		):
        super().__init__(**lowerCamelCase						)

        __a       =    do_rescale
        __a       =    rescale_factor
        __a       =    do_pad
        __a       =    pad_size


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase = None    ,		**lowerCamelCase						):
        return rescale(lowerCamelCase    ,		scale=lowerCamelCase    ,		data_format=lowerCamelCase    ,		**lowerCamelCase						)


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase = None						):
        __a ,   __a       =    get_image_size(lowerCamelCase						)
        __a       =    (old_height // size + 1) * size - old_height
        __a       =    (old_width // size + 1) * size - old_width

        return pad(lowerCamelCase    ,		((0, pad_height), (0, pad_width))    ,		mode="symmetric"    ,		data_format=lowerCamelCase						)






 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = ChannelDimension.FIRST    ,		**lowerCamelCase    ,		):
        __a       =    do_rescale if do_rescale is not None else self.do_rescale
        __a       =    rescale_factor if rescale_factor is not None else self.rescale_factor
        __a       =    do_pad if do_pad is not None else self.do_pad
        __a       =    pad_size if pad_size is not None else self.pad_size

        __a       =    make_list_of_images(lowerCamelCase						)

        if not valid_images(lowerCamelCase						):
               raise ValueError(
                   "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
                   "torch.Tensor, tf.Tensor or jax.ndarray."						)

        if do_rescale and rescale_factor is None:
               raise ValueError("Rescale factor must be specified if do_rescale is True."						)

        # All transformations expect numpy arrays.
        __a       =    [to_numpy_array(lowerCamelCase						) for image in images]

        if do_rescale:
               __a       =    [self.rescale(image=lowerCamelCase    ,		scale=lowerCamelCase						) for image in images]

        if do_pad:
               __a       =    [self.pad(lowerCamelCase    ,		size=lowerCamelCase						) for image in images]

        __a       =    [to_channel_dimension_format(lowerCamelCase    ,		lowerCamelCase						) for image in images]

        __a       =    {"pixel_values": images}
        return BatchFeature(data=lowerCamelCase    ,		tensor_type=lowerCamelCase						)

"""simple docstring"""



import os

import pytest
from attr import dataclass


SCREAMING_SNAKE_CASE__:List[str]									=			"""us-east-1"""  # defaults region






@dataclass
class  snake_case__      :
 _snake_case       :					str
 _snake_case       :					Optional[Any]							  =						"""arn:aws:iam::558105141721:role/sagemaker_execution_role"""
 _snake_case       :					Optional[Any]							  =						{
     """task_name""": """mnli""",
     """per_device_train_batch_size""": 16,
     """per_device_eval_batch_size""": 16,
     """do_train""": True,
     """do_eval""": True,
     """do_predict""": True,
     """output_dir""": """/opt/ml/model""",
     """overwrite_output_dir""": True,
     """max_steps""": 500,
     """save_steps""": 5_500,
 }
 _snake_case       :					List[str]							  =						{**hyperparameters, """max_steps""": 1_000}


 @property
 def 						a__   ( self						):
        if self.framework == "pytorch":
               return [
                   {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
                   {"Name": "eval_accuracy", "Regex": r"eval_accuracy.*=\D*(.*?)$"},
                   {"Name": "eval_loss", "Regex": r"eval_loss.*=\D*(.*?)$"},
               ]
        else:
               return [
                   {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
                   {"Name": "eval_accuracy", "Regex": r"loss.*=\D*(.*?)]?$"},
                   {"Name": "eval_loss", "Regex": r"sparse_categorical_accuracy.*=\D*(.*?)]?$"},
               ]


 @property
 def 						a__   ( self						):
        return F"{self.framework}-transfromers-test"


 @property
 def 						a__   ( self						):
        return F"./tests/sagemaker/scripts/{self.framework}"






 @property
 def 						a__   ( self						):
        if self.framework == "pytorch":
               return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-training:1.7.1-transformers4.6.1-gpu-py36-cu110-ubuntu18.04"
        else:
               return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-tensorflow-training:2.4.1-transformers4.6.1-gpu-py37-cu110-ubuntu18.04"



@pytest.fixture(scope="class"				)
def 	_lowerCamelCase(	a				):
       __a       =    SageMakerTestEnvironment(framework=request.cls.framework				)
